Hostname: page-component-76fb5796d-45l2p Total loading time: 0 Render date: 2024-04-25T17:47:12.269Z Has data issue: false hasContentIssue false
  • English
  • Français

Taking dyads seriously

Published online by Cambridge University Press:  15 November 2021

Shahryar Minhas Open the ORCID record for Shahryar Minhas [Opens in a new window] ,
Cassy Dorff ,
Max B. Gallop ,
Margaret Foster ,
Howard Liu ,
Juan Tellez  and
Michael D. Ward
Shahryar Minhas*
Affiliation:
Department of Political Science, Michigan State University, East Lansing, MI, USA
Cassy Dorff
Affiliation:
Department of Political Science, Vanderbilt University, Nashville, TN, USA
Max B. Gallop
Affiliation:
Department of Government and Public Policy, University of Strathclyde, Glasgow, Scotland, UK
Margaret Foster
Affiliation:
Department of Political Science, University of North Carolina, Chapel Hill, NC, USA
Howard Liu
Affiliation:
Department of Government, University of Essex, Colchester, England, UK
Juan Tellez
Affiliation:
Department of Political Science, University of South Carolina, Columbia, SC, USA
Michael D. Ward
Affiliation:
Department of Political Science, Duke University, Durham, NC, USA
*
*Corresponding author. Email: minhassh@msu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

International relations scholarship concerns dyads, yet standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We show how a regression-based approach, the Additive and Multiplicative Effects (AME) model, can be used to account for the inherent dependencies in dyadic data and glean substantive insights in the interrelations between actors. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME outperforms standard approaches in terms of out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars’ ability to conduct inferential analyses on dyadic data.

Keywords

Bayesianlatent variable modelsmulti-level and hierarchical models
Type
Original Article
Information
Political Science Research and Methods , Volume 10 , Issue 4 , October 2022 , pp. 703 - 721
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the European Political Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

Deceased

**

Present address: Department of Political Science, University of California, Davis, USA

References

Adamic, LA and Glance, N (2005) The political blogosphere and the 2004 US election: divided they blog. Proceedings of the 3rd International Workshop on Link Discovery. New York, NY: ACM, for ACM, pp. 36–43.CrossRefGoogle Scholar
Anderson, CJ, Wasserman, S and Faust, K (1992) Building stochastic blockmodels. Social Networks 14, 137–161.CrossRefGoogle Scholar
Aronow, PM, Samii, C and Assenova, VA (2015) Cluster-robust variance estimation for dyadic data. Political Analysis 23, 564–577.Google Scholar
Bai, J (2009) Panel data models with interactive fixed effects. Econometrica 77, 1229–1279.Google Scholar
Barabási, A-L and Réka, A (1999) Emergence of scaling in random networks. Science 286, 509–510.Google ScholarPubMed
Beck, N and Katz, JN (1995) What to do (and not to do) with pooled time-series cross-section data. American Political Science Review 89, 634–647.CrossRefGoogle Scholar
Beck, N, Katz, JN and Tucker, Richard. (1998) Taking time seriously: time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science 42, 1260–1288.CrossRefGoogle Scholar
Bennett, J and Lanning, S (2007) The Netflix prize. Proceedings of KDD Cup and Workshop.Google Scholar
Block, P, Stadtfeld, C and Snijders, TAB (2017) Forms of dependence: comparing SAOMs and ERGMs from basic principles. Sociological Methods & Research 48(1), 202–239.Google Scholar
Chyzh, O (2016) Dangerous liaisons: an endogenous model of international trade and human rights. Journal of Peace Research 53, 409–423.CrossRefGoogle Scholar
Colgan, JD (2010) Oil and revolutionary governments: fuel for international conflict. International Organization 64, 661–694.CrossRefGoogle Scholar
Davis, J and Goadrich, M (2006) The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, ICML 2006. New York, NY, USA: ACM, pp. 233–240.Google Scholar
Dorff, C and Minhas, S (2017) When do states say uncle? network dependence and sanction compliance. International Interactions 43, 563–588.CrossRefGoogle Scholar
Dorff, C, Gallop, M and Minhas, S (2020) Networks of violence: predicting conflict in Nigeria. The Journal of Politics 82, 476–493.CrossRefGoogle Scholar
Erikson, RS, Pinto, PM and Rader, KT (2014) Dyadic analysis in international relations: a cautionary tale. Political Analysis 22, 457–463.CrossRefGoogle Scholar
Gallop, M (2017) More dangerous than dyads: how a third party enables rationalist explanations for war. Journal of Theoretical Politics 29, 353–381.CrossRefGoogle Scholar
Gibler, DM (2017) State development, parity, and international conflict. American Political Science Review 111, 21–38.CrossRefGoogle Scholar
Greenhill, B, Ward, MD and Sacks, A (2011) The separation plot: a new visual method for evaluating the fit of binary data. American Journal of Political Science 55, 991–1002.CrossRefGoogle Scholar
Hoff, PD (2005) Bilinear mixed-effects models for dyadic data. Journal of the American Statistical Association 100, 286–295.CrossRefGoogle Scholar
Hoff, PD (2008) Modeling homophily and stochastic equivalence in symmetric relational data. In Platt JC, Koller D, Singer Y and Roweis ST (eds), Advances in Neural Information Processing Systems 20, Processing Systems 21. Cambridge, MA, USA: MIT Press, pp. 657–664.Google Scholar
Hoff, PD (2021) Additive and multiplicative effects network models. Statistical Science 36, 34–50.CrossRefGoogle Scholar
Keohane, RO (1989) Reciprocity in international relations. International Organization 40, 1–27.CrossRefGoogle Scholar
Kinne, BJ (2013) Network dynamics and the evolution of international cooperation. American Political Science Review 107, 766–785.CrossRefGoogle Scholar
Li, H and Loken, E (2002) A unified theory of statistical analysis and inference for variance component models for dyadic data. Statistica Sinica 12, 519–535.Google Scholar
Manger, MS, Pickup, MA and Snijders, TAB (2012) A hierarchy of preferences: a longitudinal network analysis approach to PTA formation. Journal of Conflict Resolution 56, 852–877.CrossRefGoogle Scholar
Maoz, Z (2009) The effects of strategic and economic interdependence on international conflict across levels of analysis. American Journal of Political Science 53, 223–240.CrossRefGoogle Scholar
Minhas, S, Hoff, PD and Ward, MD (2017) Influence networks in international relations. Working Paper.Google Scholar
Minhas, S, Hoff, PD and Ward, MD (2019) Inferential approaches for network analysis: AMEN for latent factor models. Political Analysis 27, 208–222.CrossRefGoogle Scholar
Mucha, PJ, Richardson, T, Macon, K, Porter, MA and Onnela, JP (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 876ff.CrossRefGoogle ScholarPubMed
Organski, AFK (1958) World Politics: The Stages of Political Development. New York: Alfred A. Knopf.Google Scholar
Pang, X (2014) Varying responses to common shocks and complex cross-sectional dependence: dynamic multilevel modeling with multifactor error structures for time-series cross-sectional data. Political Analysis 22, 464–496.CrossRefGoogle Scholar
Pawitan, Y (2013) In All Likelihood: Statistical Modeling and Inference Using Likelihood, 1st Edn. Oxford, England: Oxford University Press.Google Scholar
Peceny, M, Beer, CC and Sanchez-Terry, S (2002) Identifying the culprit: democracy, dictatorship, and dispute initiation. American Political Science Review 96, 15–26.CrossRefGoogle Scholar
Reiter, D and Stam, AC (2003) Identifying the culprit: democracy, dictatorship, and dispute initiation. American Political Science Review 97, 333–337.CrossRefGoogle Scholar
Resnick, P and Varian, HR (1997) Recommender systems. Communications of the ACM 40, 56–58.CrossRefGoogle Scholar
Richardson, LF (1960) Arms and Insecurity. Chicago and Pittsburgh, PA: Quadrangle/Boxwood.Google Scholar
Signorino, C (1999) Strategic interaction and the statistical analysis of international conflict. American Political Science Review 92, 279–298.CrossRefGoogle Scholar
Singer, JD (1972) The correlates of war project: continuity, diversity, and convergence. In Singer JD (ed), Quantitative International Politics: An Appraisal. Special Studies in International Politics and Government, Vol. VI. Praeger.Google Scholar
Wasserman, S and Faust, K (1994) Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Weeks, JL (2012) Strongmen and straw men: authoritarian regimes and the initiation of international conflict. American Political Science Review 106, 326–347.CrossRefGoogle Scholar
Zinnes, DA (1967) An analytical study of the balance of power theories. Journal of Peace Research 3, 270–288.CrossRefGoogle Scholar
Supplementary material: Link

Minhas et al Dataset

Dataset

https://doi.org/10.7910/DVN/Z6ASMM
Link
Supplementary material: PDF

Minhas et al. supplementary material

Minhas et al. supplementary material

Download Minhas et al. supplementary material(PDF)
PDF 1.2 MB